This course prepares learners to design, implement, and operate Machine Learning Operations (MLOps) and Generative AI Operations (GenAIOps) solutions on Azure. It covers building secure and scalable AI infrastructure, managing the full lifecycle of traditional machine learning models with Azure Machine Learning, and deploying, evaluating, monitoring, and optimising generative AI applications and agents using Microsoft Foundry.

Utbildningsmål

Learners will gain hands-on knowledge of automation, continuous integration and delivery, infrastructure as code, and observability by using tools such as GitHub Actions, Azure CLI, and Bicep. The course emphasises collaboration with data science and DevOps teams to deliver reliable, production-ready AI systems aligned with modern MLOps and GenAIOps best practices.

• Design and run machine learning experiments using Azure Machine Learning, including AutoML and model tracking
• Optimise model performance through hyperparameter tuning and structured experimentation
• Build and automate end-to-end ML workflows using pipelines and CI/CD with GitHub Actions
• Deploy, test, and manage machine learning models in production environments
• Implement MLOps practices to improve reliability, scalability, and repeatability of AI solutions
• Apply GenAIOps principles to develop and manage generative AI applications using Microsoft Foundry
• Manage prompts and AI agents as version-controlled assets using Git-based workflows
• Evaluate and optimise AI models and agents using structured metrics for quality, cost, and performance
• Automate AI evaluation processes to ensure continuous improvement and consistency
• Monitor AI application performance, including latency, usage, and cost
• Analyse and debug AI systems using tracing and observability techniques to improve reliability

Målgrupp

This course is intended for experienced data scientists, machine learning engineers, and DevOps professionals responsible for designing, deploying, and operating enterprise AI solutions on Azure. It is well suited for learners with professional experience in Python, a working understanding of machine learning fundamentals, and familiarity with modern DevOps practices. Participants will benefit most if they are preparing to operationalise MLOps and GenAIOps workflows using Azure-native services in production environments.

Förkunskaper

• Working knowledge of Python or R programming
• Experience developing and training machine learning models
• Familiarity with Azure Machine Learning concepts and workflows
• Understanding of core generative AI concepts and Azure AI services

Innehåll

Experiment with Azure Machine Learning

• Introduction
• Preprocess data and configure featurisation
• Run an automated machine learning experiment
• Evaluate and compare models
• Configure MLflow for model tracking in notebooks
• Train and track models in notebooks
• Evaluate models with the Responsible AI dashboard
• Exercise: Find the best classification model with Azure Machine Learning

Perform Hyperparameter Tuning with Azure Machine Learning

• Introduction
• Define a search space
• Configure a sampling method
• Configure early termination
• Use a sweep job for hyperparameter tuning
• Exercise: Run a sweep job

Run Pipelines in Azure Machine Learning

• Introduction
• Create components
• Create a pipeline
• Run a pipeline job
• Exercise: Run a pipeline job

Trigger Azure Machine Learning Jobs with GitHub Actions

• Introduction
• Understand the business problem
• Explore the solution architecture
• Use GitHub Actions for model training
• Exercise

Trigger GitHub Actions with Feature-Based Development

• Introduction
• Understand the business problem
• Explore the solution architecture
• Trigger a workflow
• Exercise

Work with Environments in GitHub Actions

• Introduction
• Understand the business problem
• Explore the solution architecture
• Set up environments
• Exercise

Deploy a Model with GitHub Actions

• Introduction
• Understand the business problem
• Explore the solution architecture
• Model deployment
• Exercise

Plan and Prepare a GenAIOps Solution

• Introduction
• Explore use cases for GenAIOps
• Select the right generative AI model
• Understand the development lifecycle of a language model application
• Explore available tools and frameworks to implement GenAIOps
• Exercise: Compare language models from the model catalog

Manage Prompts for Agents in Microsoft Foundry with GitHub

• Introduction
• Apply version control to prompts
• Understand Microsoft Foundry agents and prompt versioning
• Organise prompts in GitHub repositories
• Develop safe prompt deployment workflows
• Exercise: Develop prompt and agent versions

Evaluate and Optimise AI Agents Through Structured Experiments

• Introduction
• Design evaluation experiments
• Apply Git-based workflows to optimisation experiments
• Apply evaluation rubrics for consistent scoring
• Exercise: Evaluate and compare AI agent versions

Automate AI Evaluations with Microsoft Foundry and GitHub Actions

• Introduction
• Understand why automated evaluations matter
• Align evaluators with human criteria
• Create evaluation datasets
• Implement batch evaluations with Python
• Integrate evaluations into GitHub Actions
• Exercise: Set up automated evaluations

Monitor Your Generative AI Application

• Introduction
• Why monitoring matters
• Understand key metrics to monitor
• Explore monitoring with Azure
• Integrate monitoring into your application
• Interpret monitoring results
• Exercise: Enable monitoring for a generative AI application

Analyse and Debug Your Generative AI Application with Tracing

• Introduction
• Why tracing is important
• Identify what to trace in generative AI applications
• Implement tracing in generative AI applications
• Debug complex workflows with advanced tracing patterns
• Analyse trace data to inform decisions
• Exercise: Enable tracing for a generative AI application 

Diplom

Upon completion of the training, you will receive a diploma.

Anpassning

Yes, we are happy to tailor the training to your specific needs.

Certifiering

A certification voucher is included in the price. Valid for one attempt.

Språk

English for open course schedules. Swedish for customized training.